Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed as federated x learning, where x includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.

中文翻译：

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联合学习的新兴趋势：从模型融合到联合X学习

联合学习是一种新的学习范式，它通过多方计算和模型聚合将数据收集和模型训练分离。作为一种灵活的学习环境，联合学习有可能与其他学习框架集成。我们结合其他学习算法对联合学习进行了重点调查。具体来说，我们探索了各种学习算法来改进香草联合平均算法，并回顾了模型融合方法，例如自适应聚合，正则化，聚类方法和贝叶斯方法。跟随新兴趋势，我们还将在称为联合x学习的其他学习范式的交集中讨论联邦学习，其中x包括多任务学习，元学习，转移学习，无监督学习和强化学习。